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Review of Computer Engineering Research

June 2018, Volume 5, 2, pp 49-56

Characterisation of Propagation Loss for a 3G Cellular Network in a Crowded Market Area Using CCIR Model

Simeon Ozuomba


Enyenihi, Johnson


Ngwu Chinyere Rosemary

Simeon Ozuomba 1

Enyenihi, Johnson 2
Ngwu Chinyere Rosemary 3

  1. Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria 1

  2. Department of Electrical/Electronic Engineering, Akwa Ibom State University Mkpat Enin, Akwa Ibom State, Nigeria 2

  3. Department of Computer Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria 3

Pages: 49-56

DOI: 10.18488/journal.76.2018.52.49.56

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Article History:

Received: 08 October, 2018
Revised: 13 November, 2018
Accepted: 19 December, 2018
Published: 14 January, 2019


In this paper, the propagation loss for 1800 MHz cellular network in a crowded market is studied and characterized using the Comit´e International des Radio-Communication, (CCIR) propagation loss model. Empirical measurement of the received signal strength in the market was conducted using CellMapper android app installed on Samsung Galaxy S4 phone. The CCIR model was configured with three different percentages of covered areas (PB). The model was optimized using the root means square error (RMSE) method and also by tuning the PB value. The un-tuned CCIR model gave an RMSE value of 9.23 dB which is above the acceptable upper limit of 6 dB for propagation loss prediction models. On the other hand, the PB-tuned CCIR model gave the best prediction result with an RMSE value of 2.177 dB and prediction accuracy of 98.11 % which is better than the performance of all the RMSE-tuned CCIR models. The results showed that apart from using an RMSE value to tune the CCIR propagation loss model, adjustment of some other key parameters of the model can as well provide a better prediction performance. However, the choice of the parameter to be tuned depends on the specific nature of the case study area.
Contribution/ Originality
The paper's primary contribution is the development of an alternative approach for optimizing the CCIR model by adjusting the percentage of covered area rather than using the root mean square error (RMSE). The paper demonstrated that the proposed method can give better propagation prediction performance than the RMSE-based optimization approach.


Propagation loss, Cellular network, CCIR model, RMSE-tuned model, Model tuning, Empirical model.



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This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.


All authors contributed equally to the conception and design of the study.

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